• Trity Course Scilab IoT

    Scilab for the Internet of Things

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  • Trity Course RPi IoT

    Raspberry Pi for the Internet of Things (with Pi-3)

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  • Trity Course Scilab AI

    Artificial Intelligence with Scilab

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  • Trity Course Scilab NCV

    Numerical Computation and Visualization with Scilab

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  • Trity Course Scilab IP

    Scilab for Image Processing and Computer Vision

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  • Trity Course Scilab BDA

    Big Data Training Series : Practical Guide to Big Data Analytics with Pig Latin, Hive and Scilab

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  • Trity Course Scilab DM

    Scilab for Data Mining

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  • Python Deep Learning

    Python for Machine and Deep Learning

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Scilab Courses

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Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. It can be used for signal and image processing, statistical analysis, Internet of Things, data mining, etc. In Trity Technologies we have developed more than 20 courses based on Scilab since last few years.

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Raspberry Pi Courses

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The Raspberry Pi is a series of credit card–sized single-board computers developed in the United Kingdom by the Raspberry Pi Foundation with the intent to promote the teaching of basic computer science in schools and developing countries. Our very first Raspberry Pi Training is the aplication in IoT, and we are extending the training into other fields from time to time. 

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E4Coder - Automatic Code Generation

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E4Coder is a set of tools that can be used to simulate control algorithms and to generate code for embedded microcontrollers running with or without a realtime operating system. Our course focus on using the block diagram for algorithms development and the codes would be automatically generated and downloaded into the embedded boards such as Arduino Uno. A mobile robot application would be used for the training for practical hands-on. 

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Numerical Methods with Scilab


Practical Implementation of Scilab for Numerical Methods

Using open source Scilab for numerical methods studies and researches prepared the students for better job market competitiveness.

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Scientific computing is playing a prominent role as a tool in scientific discovery and engineering analysis. Open Source encourages the sharing of knowledge and accelerates the discovery and analysis.”

Course Synopsis


Numerical analysis is the study of algorithms that use numerical approximation for the problems of mathematical analysis. It naturally finds applications in all fields of engineering and the physical sciences, but in the 21st century, the life sciences and even the arts have adopted elements of scientific computations.

The overall goal of the field of numerical analysis is the design and analysis of techniques to give approximate but accurate solutions to hard problems. Before the advent of modern computers numerical methods often depended on hand interpolation in large printed tables. Since the mid 20th century, computational softwares such as Scilab calculates, simulates and design the required functions instead.


Course Objectives


This is a 3 full-day course which allows participant to learn Scilab fundamentals and implementing important numerical and computational methods for solving engineering and scientific problems. The course will include refresher on linear algebra, exploring methods for solving linear and nonlinear equations, evaluating integrals and solving ordinary differential equations. 

Who Must Attend

Researchers, Lecturers, Scientists, Engineers and Managers that are keen in using Scilab especially in numerical analysis. This hands-on course is specifically designed for those who are interested in the area of technical computing.


No background required. Preferably with experience in basic computer operations


Course Outline

Scilab Environment

  • Getting Started with Scilab
  • Simple Operations with Scilab
  • Special Constants in Scilab
  • Number, String and Boolean Arrays

Scilab Programming

  • Loops & Conditional Constructs
  • SciNotes Editor
  • Script File (*.sce)
  • Function File (*.sci)
  • Inline Function
  • Strings in Scilab
  • Case Study

Plotting and Visualization

  • 2-D Plotting
  • Labeling and Annotating Graphs
  • Surface Plot (3-D)
  • Scilab Demonstrations
  • Case Study


  • Floating point arithmetic and Errors in Scilab
  • Vector Operations with
  • Scilab Dot & Cross Product
  • Cartesian & Polar Representation of Vectors
  • Case Study

Matrices and Linear Algebra

  • Matrix Operations
  • Special Matrices, Vandermode, Hilbert and Magic
  • Matrix Manipulation
  • Matrix Characterization (Decomposition, SVD, Rank, Determinant, Eigenvectors, Eigenvalues)
  • LU Factorization & Cramer’s rule
  • Solutions of Linear Equations
  • Gaussian Elimination & Gauss-Jordan Elimination 
  • Jacobi & Gauss-Siedel Iteration
  • Case Study

Root Finding & Solution to Non-Linear Equations

  • Complex Number Operations
  • Solution to Polynomial Equations
  • Bisection Method
  • Newton-Raphson Method
  • Secant Method
  • Fixed Point Iteration
  • Other Scilab functions
  • Case Study

Numerical Integration

  • Definitions and Concepts
  • Trapezoid Rule
  • Simpson Rule
  • Other Scilab integrating functions
  • Applications of Integrals of One Variable
  • Double Integrals
  • Case Study

Ordinary Differential Equation

  • Definitions and Concepts
  • Solving ODE – Initial Value Problem
  • Higher Order Derivatives
  • ODE Solver with root finding
  • Type of Methods (Adams, BDF & Runge-Kutta)
  • Solving ODE – Boundary Value Problem
  • Solving Differential Algebraic Equations

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